End Notebook Chaos Forever

🗺️ Geospatial + Data Engineering Workloads on Apache Spark

Master both geographic transformations and standard data pipelines with a professional framework for organizing Apache Sedona Spark SQL queries.

Integrates seamlessly into your existing CI/CD, processes, and infrastructure. SketchMyView brings GeoHarbor to your team, whether we work with your existing engineers or drive implementation independently.

See How It Works Schedule a Consultation
🗺️ Apache Sedona SQL 🗺️ Geospatial Ready ⚡ Apache Spark Native ✨ Enterprise Grade 🚀 Production Ready ⚡ 70% Faster Development

Does This Sound Familiar?

Whether you're doing geospatial analysis or standard data engineering, the nightmare is the same. Thousands of data teams face it every day.

😱

Notebook Hell

73 notebooks named "final_v3_FIXED_USE_THIS.ipynb" and nobody knows which one actually runs in production, Sedona queries scattered everywhere.

🎨

No Standards

Every engineer writes Spark SQL queries their own way. Different approaches for geo operations, standard transforms, data cleaning. Copy-paste is your code reuse strategy.

💥

Schema Chaos

Schemas buried in code. Geometry columns mixed with standard columns. Three different date formats. No consistency. Hope and pray it works.

🍝

SQL Spaghetti

500-line Sedona Spark SQL queries embedded in notebooks and Python files, duplicated across 20 notebooks. Good luck maintaining or understanding that.

⏰

Onboarding Nightmare

New engineers take 3 weeks just to understand the codebase. Even longer if they need to learn Sedona SQL patterns on top. Most consider quitting.

🔥

Production Fires

Friday night disasters. Can't find the Sedona query that's causing the issue. Performance is unpredictable. Takes 8 hours to fix because logic is scattered everywhere.

Meet Sarah, Your New Data Engineer (Geospatial Edition)

It's her first day. She's excited to build data pipelines. Her manager gives her a task: "We need to analyze delivery routes across regions and create a monthly sales report by geographic area."

She opens the project folder...

"There are 73 Jupyter notebooks here. Each one has a name like 'geo_data_final_v3_FIXED_really_final_USE_THIS_ONE.ipynb'. Spark SQL queries are scattered everywhere. Some use Sedona for geospatial work, some use raw Spark. Schema definitions mix geographic columns with standard data, with no consistency. I see different coordinate reference systems being used without documentation. The Sedona queries are embedded in Python code with no structure. I have absolutely no idea where to start or which query does what."
— Sarah, 30 minutes into her first day

Three weeks later: Sarah is still struggling. She's considering quitting. Your team has lost three engineers this year for the same reason.

The cost? $300K in turnover, countless hours of lost productivity, failed geospatial analyses, and a data platform that's getting worse every day.

There's a Better Way

Imagine Sarah's first day with GeoHarbor—unified structure for Sedona queries and standard Spark SQL transformations

😱 The Old Way

  • 73 notebooks with random names
  • Spark SQL scattered everywhere
  • Sedona queries mixed with standard SQL
  • Inconsistent patterns & approaches
  • No CRS documentation
  • Schemas buried in code
  • SQL queries embedded in notebooks
  • No organization or structure
  • 3 weeks to onboard
  • Unpredictable performance
  • Frequent production failures
  • Engineers want to quit

✨ With GeoHarbor

  • Clean, organized structure
  • All Spark SQL in one place
  • Sedona & standard SQL organized together
  • Enforced patterns & best practices
  • CRS documented & consistent
  • Schemas in datacatalog/
  • SQL in sqlcatalog/
  • Clear structure everyone understands
  • Productive in 4 hours
  • Predictable query performance
  • Fits your existing ecosystem
  • Works with your CI/CD

Real Results from Real Teams

70%
Faster Pipeline Development
4 hrs
To Onboard New Engineers
3x
Faster Geospatial Query Execution
$2M
Saved Over 3 Years

What GeoHarbor Actually Does

A unified framework for organizing Sedona Spark SQL queries and standard data transformations

🗺️

Sedona Queries SEDONA

Organized structure for Apache Sedona Spark SQL geospatial queries. Consistent patterns for geographic transformations, CRS handling, and spatial operations at scale.

📊

Standard SQL Pipelines

Handle tabular data transformations and business logic Spark SQL with the same unified structure and organization as your geospatial work.

📁

Organized Structure

Schemas in one place. SQL queries in another. Job configs separate. Geospatial definitions isolated. Everyone knows where everything is and how it works.

🎯

Enforced Standards

One way to write Spark SQL queries. One pattern for Sedona geospatial work. Consistency across all pipelines—geographic and standard alike.

✅

Query Reusability

SQL queries (Sedona or standard) written once, reused everywhere. No more copy-paste. No more duplicated logic scattered across notebooks.

🌍

CRS Management SEDONA

Standardized approach to handling coordinate reference systems in Sedona queries. Documented, consistent, and understood by your entire team.

📊

Data Governance

Automatic lineage tracking. Clear audit trails. Compliance-ready documentation. Know exactly where your data comes from and where it goes.

⚡

Rapid Development

New pipelines in 30 minutes instead of 2 days. Write and reuse SQL queries quickly. Sedona or standard, the structure makes it fast.

🚀

Production Ready

Battle-tested patterns. Proven Spark SQL and Sedona best practices. Optimized query organization. Scales to millions of geospatial records seamlessly.

Works Everywhere Spark Runs

GeoHarbor is platform-agnostic and works with any Spark infrastructure. Deploy on your preferred platform.

☁️

Azure Synapse

Apache Spark pools in Azure Synapse Analytics

🧵

Microsoft Fabric

Spark compute in Microsoft Fabric workspace

🔷

Databricks

Databricks clusters and Delta Lake

🏢

On-Premise

Self-managed Apache Spark clusters

💼

AWS EMR

Elastic MapReduce Spark clusters

🔥

Google Cloud

Dataproc and Vertex AI Spark

Fits Your Existing Ecosystem

GeoHarbor is a framework for organizing queries, not a platform that dictates how you work

No Disruption. No Changes Required.

GeoHarbor integrates seamlessly with your current infrastructure and processes. You're not ripping out your CI/CD pipeline or changing your deployment model.

  • Your CI/CD stays the same — GeoHarbor works with GitHub Actions, GitLab CI, Jenkins, Azure Pipelines, or whatever you use today
  • Your deployment process unchanged — Orchestrate with Airflow, Prefect, Databricks Workflows, or your current tooling
  • Your authentication intact — Use your existing Azure AD, IAM, or Kerberos setup
  • Your monitoring and alerting — Integrate with Datadog, New Relic, Azure Monitor, or your current stack
  • Your data ownership — Schemas and queries remain your responsibility. GeoHarbor just organizes them
  • Your existing tools work — Notebooks, IDEs, SQL editors, version control all unchanged

GeoHarbor is a Framework, Not a Platform

It provides structure and patterns for Spark SQL queries. It doesn't force you into a specific CI/CD tool, deployment method, or operational process. You keep complete control of your infrastructure and workflows.

Minimal Operational Overhead

One configuration file. Clean directory structure. Standard SQL catalogs. That's it. Everything else deployment, orchestration, monitoring is yours to decide.

Scales With Your Team

Whether you're 3 engineers or 30, GeoHarbor grows with you. No vendor lock-in. No proprietary dependencies. Just organized Spark SQL.

Consulting Engagements with GeoHarbor

GeoHarbor is delivered through professional consulting engagements. License and implementation are included when you partner with SketchMyView.

Ready to End the Chaos?

Master geospatial and standard data engineering at scale with organized Spark SQL and Sedona queries. Start a consulting engagement with SketchMyView and get GeoHarbor installed and licensed for your team.

Schedule a Consultation Get More Information

Talk to the SketchMyView team about GeoHarbor

📧 hello@sketchmyview.com

📍 London, United Kingdom